Primary Care

The ethical, legal and social implications of using artificial intelligence systems in breast cancer care.

Breast cancer care is a leading area for development of artificial intelligence (AI), with applications including screening and diagnosis, risk calculation, prognostication and clinical decision-support, management planning, and precision medicine. We review the ethical, legal and social implications of these developments. We consider the values encoded in algorithms, the need to evaluate outcomes...

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.

We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. 1) Our network's novel two-stage architecture a...

Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning.

Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high...

IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.

Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Scre...

Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms.

AIM: To assess the potential of supervised machine-learning techniques to identify clinical variables for predicting short-term and long-term glycated...

Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning.

BACKGROUND: Advanced machine learning methods combined with large sets of health screening data provide opportunities for diagnostic value in human an...

[E-health and "Cancer outside the hospital walls", Big Data and artificial intelligence].

To heal otherwise in oncology has become an imperative of Public Health and an economic imperative in France. Patients can therefore receive live most...

Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques.

INTRODUCTION: As one of the most prevalent chronic diseases in the United States, diabetes, especially type 2 diabetes, affects the health of millions...

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers.

In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challenging. Although semi-automatic systems to modulate...

Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score.

OBJECTIVE: To develop and validate a novel, machine learning-derived model to predict the risk of heart failure (HF) among patients with type 2 diabet...

Artificial intelligence and breast screening: French Radiology Community position paper.

The objective of this article was to evaluate the evidence currently available about the clinical value of artificial intelligence (AI) in breast imag...

Continuous Glucose Monitoring and Glycemic Control in Patients With Type 2 Diabetes Mellitus and CKD.

RATIONALE & OBJECTIVE: The accuracy of glycated hemoglobin (HbA) level for assessment of glycemic control in patients with chronic kidney disease (CKD...

Prediction and Experimental Confirmation of Novel Peripheral Cannabinoid-1 Receptor Antagonists.

Small molecules targeting peripheral CB1 receptors have therapeutic potential in a variety of disorders including obesity-related, hormonal, and metab...

Deep Learning to Improve Breast Cancer Detection on Screening Mammography.

The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging probl...

Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records.

OBJECTIVE: Diabetes is responsible for considerable morbidity, healthcare utilisation and mortality in both developed and developing countries. Curren...

Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder.

Computational drug repositioning, designed to identify new indications for existing drugs, significantly reduced the cost and time involved in drug de...

Prediction model development of late-onset preeclampsia using machine learning-based methods.

Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the lack of effective preventive measures, its predict...

Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data.

Colorectal cancer (CRC) is third in prevalence and mortality among all cancers in the US. Currently, the United States Preventative Services Task Forc...

The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes.

Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g.,...

Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships.

In this work, computational compound screening strategies on the basis of two- and three-dimensional (2D and 3D) molecular representations were invest...